Learning Naı̈ve Bayes Classifiers From Attribute Value Taxonomies and Partially Specified Data
نویسندگان
چکیده
Partially specified data are commonplace in many practical applications of machine learning where different instances are described at different levels of precision relative to an attribute value taxonomy (AVT). This paper describes AVTNBL an extension of the Naı̈ve Bayes Learning algorithm that effectively exploits user-supplied attribute value taxonomies to construct compact and accurate Naı̈ve Bayes classifiers from partially specified data. Our experiments with benchmark data sets and AVTs show that AVT-NBL yields classifiers that are substantially more accurate and more compact than those obtained using the standard Naı̈ve Bayes learner.
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